Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption.
Fiche du document
11 octobre 2021
- doi: 10.1038/s41467-021-25972-y
- issn: 2041-1723
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1038/s41467-021-25972-y
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/pmid/34635645
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/eissn/2041-1723
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:serval-BIB_3683F2C630902
info:eu-repo/semantics/openAccess , CC BY 4.0 , https://creativecommons.org/licenses/by/4.0/
Sujets proches
Methods of analysis Analysis and chemistry Analytical methods Chemical analysis Analysis methods Analysis and examinationCiter ce document
D. Froelicher et al., « Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption. », Serveur académique Lausannois, ID : 10.1038/s41467-021-25972-y
Métriques
Partage / Export
Résumé
Using real-world evidence in biomedical research, an indispensable complement to clinical trials, requires access to large quantities of patient data that are typically held separately by multiple healthcare institutions. We propose FAMHE, a novel federated analytics system that, based on multiparty homomorphic encryption (MHE), enables privacy-preserving analyses of distributed datasets by yielding highly accurate results without revealing any intermediate data. We demonstrate the applicability of FAMHE to essential biomedical analysis tasks, including Kaplan-Meier survival analysis in oncology and genome-wide association studies in medical genetics. Using our system, we accurately and efficiently reproduce two published centralized studies in a federated setting, enabling biomedical insights that are not possible from individual institutions alone. Our work represents a necessary key step towards overcoming the privacy hurdle in enabling multi-centric scientific collaborations.